Issues regarding privacy have been forefront within the online space, business transactions and government decisions. This is mainly in response to the scandals, breaches and personal data leaks that have been notorious within technology and information systems.
It is true that privacy is a crucial aspect of cybersecurity and these issues must be addressed properly and effectively in order to address the lack of trust people place in information systems. In order to achieve this, it is crucial to ensure that technological advancements do not threaten privacy but rather, enhance privacy assurance by implementing safety and security to protect personal data.
A crucial aspect in future technology advancements and online security is the growing development and application of artificial intelligence (AI). However, privacy principles should be considered at the beginning of the AI development process to balance technological benefits while safeguarding privacy.
Privacy Considerations for AI
Let’s delve deeper and explore the impact and potential effects of increased online AI implementation. Even though it seems like a science fiction concept, when AI starts to ‘think’ similar to humans, or sometimes in place of humans, it can potentially jeopardize the three main privacy principles – data accuracy, protection and control:
Data accuracy: In order for AI to formulate accurate outcomes, algorithms should contain massive and representative data sets. Underrepresentation of some groups of data sets can lead to inaccurate results and also harmful decisions as a result. This algorithmic bias often occurs unintentionally. For instance, recent studies have shown that smart speakers tend to mishear or fail to understand female and minority voices as the algorithms used in them were developed using databases that had primarily only white male voices.
Even though massive data sets provide highly accurate and representative outcomes, they tend to have higher privacy risks if they get breached. Personal data that has seemingly been encrypted can be easily decrypted using AI. In particular, studies have shown that even minimal anonymity in coarse data sets can result in up to 95% reidentification. All in all, this can potentially have the risk of being easily identified and lead to a data leak if privacy considerations are not implemented early on. AI can also lead to red flags when it is used to process taxes and process federal benefits eligibility.
When AI begins to detect and define patterns, it formulates conclusions and can make decisions about your behavior into account to make your online experience simpler and more robust. But, when AI produces false or unfavorable results, it raises doubts on whether the decisions were done fairly. For instance, AI used to measure credit risks can unintentionally stop the credit lines of people that fit certain profiles. These decisions can occur without any warning, consent or choice, particularly when the data powering these decisions is gathered without your knowledge. Additionally, AI can formulate further details about you, like political preferences, race and religion, even if you have not specifically broadcast these details online.
The bottom line is that personal data can be utilized and also utilized against you, without any control. However, developers can decrease challenges related to privacy in the development stage, well before production. Doing so, allows developers to leverage the technological benefits of AI without violating people’s privacy. To facilitate increased privacy, it is vital to add AI within a companies data governance strategy and assigning resources to not just AI product development but to AI privacy, security and monitoring as well.
Additional methods of privacy protection in AI include:
- Implement good data hygiene. Only the types of data required to create the AI should be collected, and the data must be stored securely and only kept for the duration it is required to accomplish its purpose.
- Use good data sets. Developers must develop AI using accurate, fair and representative data sets. Wherever possible, developers must create AI algorithms that are capable of ensuring and auditing the quality of other algorithms.
- Give users control.Users should be alerted whenever their data is used, whether AI is being used to carry decisions about them and whether their data is used in the development of AI. They must also retain the choice of consent to such data usage.
- Reduce algorithmic bias.Make sure that data sets are inclusive and broad when ‘teaching’ the AI application. Algorithmic bias creates challenges most commonly for women, minorities and people with disabilities, that only make up a small part of the technology workforce and hence there is very little representation of them.